DOAJ
Open Access
2021
Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials
Iason Batzianoulis
Fumiaki Iwane
Shupeng Wei
Carolina Gaspar Pinto Ramos Correia
Ricardo Chavarriaga
+2 lainnya
Abstrak
Teaching an assistive robotic manipulator to move objects in a cluttered table requires demonstrations from expert operators, but what if the experts are individuals with motor disabilities? Batzianoulis et al. propose a learning approach which combines robot autonomy and a brain-computer interfacing that decodes whether the generated trajectories meet the user’s criteria, and show how their system enables the robot to learn individual user’s preferred behaviors using less than five demonstrations that are not necessarily optimal.
Topik & Kata Kunci
Penulis (7)
I
Iason Batzianoulis
F
Fumiaki Iwane
S
Shupeng Wei
C
Carolina Gaspar Pinto Ramos Correia
R
Ricardo Chavarriaga
J
José del R. Millán
A
Aude Billard
Format Sitasi
Batzianoulis, I., Iwane, F., Wei, S., Correia, C.G.P.R., Chavarriaga, R., Millán, J.d.R. et al. (2021). Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials. https://doi.org/10.1038/s42003-021-02891-8
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2021
- Sumber Database
- DOAJ
- DOI
- 10.1038/s42003-021-02891-8
- Akses
- Open Access ✓